A number of diseases are presently diagnosed using classical cytopathology methods involving examination of nuclear and cellular morphology and staining patterns. Typically, this occurs by the examination of up to 10,000 cells in a sample and the finding of about 10 to about 50 cells that are abnormal. This finding is based on subjective interpretation of visual microscopic inspection of the cells in the sample.
An example of this diagnostic methodology is the Papanicolaou smear (Pap smear). Monitoring the onset of cervical disease by detecting premalignant and malignant cells using the Pap smear has greatly reduced the mortality rate due to cervical cancer. Nevertheless, the process of screening Pap smears is labor intensive and has changed little since it was first described by Papanicolaou almost 50 years ago. To perform the test, endo- and ectocervical exfoliated cells from a patient's cervix are first scraped using a brush and spatula or a cytology broom. Because cervical disease often originates from the cervical transformation zone, i.e., the border between the endocervix (covered by glandular or columnar epithelial cells) and the ectocervix (covered by stratified squamous epithelial cells), cells from this area are sampled by the exfoliation procedure. The scraping is then smeared, or otherwise deposited, on a slide, and the slide is stained with hematoxylin/eosin (H&E) or a “Pap stain” (which consists of H&E and several other counterstains), and microscopically examined. The microscopic examination is a tedious process, and requires a cytotechnologist to visually scrutinize all the fields within a slide to detect the often few aberrant cells in a specimen. This process can be analogized to looking for needles in haystacks where most haystacks contain few if any needles. Consequently, the detection of abnormal specimens depends on the level of a cytotechnologist's experience, quality of the smear preparation, and the work load. As a result of these concerns, attempts have been made both to automate the Pap screening process, and develop other objective alternatives. Recent developments in classical cytology have focused on preparing better cell deposits, eliminating clumps of cells, and confounding materials such as mucus, erythrocytes etc.
Other techniques focus on improving the diagnostic step, which relies on visual inspection by the cytologist. Automated image analysis systems have been introduced to aid cytologists in the visual inspection of cells. These methods aid in selecting cells that need further human inspection by eliminating the most “normal” cells from the cell population. However, these techniques are expensive, labor intensive, and do not aid in all desirable cell diagnoses.
Consequently, a need exists for improvements in diagnostic techniques. In particular, there remains a need for an improved system and method for data acquisition, inspection, and comparison of cytological cellular data.